- Added Jackson annotations for JSON property handling in Chroma API request/response records.
Use @JsonInclude(JsonInclude.Include.NON_NULL) to ignore empty fields such as null where.
Use @JsonProperty(XYZ) to provide a strong contract with the Cohere API.
- Updated the Docker image for running Chroma locally and in tests to version 0.5.20.
- Enhanced methods with non-null assertions for improved code safety.
- Added a simple search test to verify document similarity search functionality.
Fixes#1749
Fixes: #1753https://github.com/spring-projects/spring-ai/issues/1753
- Add character-based truncation (max 2048 chars) for Cohere embedding requests
- Support both START and END truncation strategies
- Add unit tests verifying truncation behavior for both strategies
Truncation is applied before sending requests to Bedrock API to avoid
ValidationException when text exceeds maximum length. The END strategy
(default) keeps the first 2048 characters while START keeps the last
2048 characters.
Implemented a new configuration property 'useKeylessAuth' to toggle
between API key and Azure default credential authentication.
Added necessary dependencies and updated tests to reflect the new changes.
co-authored-by: mattgotteiner <mattgotteiner@users.noreply.github.com>
- Fix the embedding dimension configuration for opensearch client indices mapping
The dimension config is obtained by the underlying embedding model's dimension
- Updated OpenSearch mapping JSON to include dynamic embedding dimension.
- Changed default mapping for OpenSearch vector store to use generic dimension placeholder.
- Fixed OpenSearchVectorStoreAutoConfiguration to fallback to new default mapping if no custom mapping is provided.
- Added test for verifying mapping configuration with OpenSearch vector store.
- Added test dependencies for Ollama integration.
- Refactored OpenSearch integration tests to use new mapping and dimension logic.
Add tests
- Verify the mappingJson field is correctly set - verify the override works fine
- Add integration tests with Ollama embedding model
Resolves#1589
* Add property `spring.ai.vectorstore.opensearch.aws.domain-name`
* Require `AwsCredentialsProvider` to enable `AwsOpenSearchConfiguration`
* Add Testcontainers Service Connection support
* Add Docker Compose Service Connection support
Add support for no-argument Supplier and single-argument Consumer function
callbacks in the Spring AI core module. This enhancement allows:
- Registration of Supplier<O> callbacks with no input (Void) type
- Registration of Consumer<I> callbacks with no output (Void) type
- Support for Kotlin Function0 (equivalent to Java Supplier)
- Handle empty properties for Void input types in schema generation
- Enhance FunctionCallback builder to support Supplier/Consumer patterns
Additional changes:
- Add test coverage for both Supplier and Consumer callbacks in various scenarios
- Enhance TypeResolverHelper to support Consumer input type resolution
- Support lambda-style function declarations for improved ergonomics
- Add test cases for void input/output handling in OpenAI chat model
- Include examples of function calls without return values
- Add support for parameterless functions through Supplier interface
Add comprehensive documentation for the FunctionCallback API:
- Overview of the interface and its key methods
- Builder pattern usage with function and method invocation approaches
- Examples for different function types (Function, BiFunction, Supplier, Consumer)
- Best practices and common pitfalls
- Schema generation and customization options
Resolves#1718 , #1277 , #1118, #860
Improves function calling documentation with clearer organization and examples:
- Reorganizes content into server-side and client-side registration sections
- Adds detailed examples for both function-invoking and method-invoking approaches
- Enhances tool context documentation with diagrams and usage examples
- Updates function-calling diagrams to reflect current implementation
Introduces a simplified, type-safe builder pattern for function callbacks to
improve developer experience and code reliability. The new hierarchical API
separates concerns between direct function invocation and method reflection,
while providing better compile-time safety.
This change deprecates the older FunctionCallbackWrapper in favor of a more
intuitive FunctionCallback.Builder that better handles generic types via
ParameterizedTypeReference. It also adds automatic function description
generation as a fallback when none is provided, though explicit descriptions
are still recommended.
The update standardizes function callback handling across all AI model
implementations (OpenAI, Ollama, Minimax, etc.) and improves response
handling with configurable converters.
Core API Enhancements:
- New Builder Interface: Replaced FunctionCallbackWrapper.builder() with
FunctionCallback.builder(), introducing a hierarchical approach that improves
customization and type safety.
- Specialized Builders: Introduced FunctionInvokerBuilder for direct Function/BiFunction
implementations and MethodInvokerBuilder for reflection-based invocations.
- Generic Type Support: Added ParameterizedTypeReference for better handling of generic parameters.
- Unified Method Definition: Merged method() and argumentTypes() into a single method() call
for simplicity and type safety.
- Automatic Descriptions: Implemented auto-generation of function descriptions, with warnings
to encourage explicit descriptions.
- Configurable Response Converters: Enhanced response handling with support for custom
converters, reducing unnecessary JSON conversions.
Architecture Improvements:
- Established common Builder interface for shared properties
- Separated function object handling from constructor
- Added method-specific configuration (name, arg types, target)
- Added JSON schema generation support for ResolvableType
- Moved to standardized schema types across AI providers
- Set OPEN_API_SCHEMA as default for Vertex AI Gemini
Builder Pattern Standardization:
- Standardized builder method ordering across implementations
- Moved function() call after description() for consistency
- Improved function callback configuration with unified patterns
- Enhanced error handling and validation in DefaultFunctionCallbackBuilder
Deprecations:
- FunctionCallbackWrapper.Builder replaced by DefaultFunctionCallbackBuilder
- Removed CustomizedTypeReference in favor of ParameterizedTypeReference
- Deprecated older ChatClient API methods for function handling
Testing & Documentation:
- Updated all AI model implementations (OpenAI, Ollama, Minimax, Moonshot, ZhiPuAI)
- Added comprehensive integration tests for static/instance methods
- Added integration tests for auto-generated descriptions
- Updated documentation to reflect new builder pattern usage
- Added Kotlin extension for inputType() support
Co-authored-by: Sébastien Deleuze <sebastien.deleuze@broadcom.com>
This change enables more flexible integration between Spring AI and LLM function
calling capabilities while maintaining type safety and ease of use.
- Add new MethodFunctionCallback class to support method invocation via reflection
- Supports both static and non-static method calls
- Handles multiple parameter types including primitives, objects, collections
- Supports empty parameters and empty response
- Auto-generates JSON schema from method parameters
- Special handling for ToolContext parameters
- Builder pattern for easy configuration
- Add comprehensive unit tests for MethodFunctionCallback
- Add integration tests for MethodFunctionCallback with both Anthropic and OpenAI clients
- Add jackson-module-jsonSchema dependency
- Modify FunctionCallback to check for empty tool context
Testing coverage includes:
- Static method invocation scenarios
- Non-static method calls with various parameter types
- Void return type methods
- Complex parameter types (enums, records, lists)
- Tool context handling
- Error cases and validation
Add MethodFunctionCallback reference docs
- Add migration note recommending transition from InvokeModel to Converse API
- Update model configuration to remove default model ID
- Add AWS session token configuration for temporary credentials
- Improve documentation links and descriptions
- Add support for proper Kotlin functions handling by adapting
`kotlin.jvm.functions.Function1` to `java.util.function.Function` and
`kotlin.jvm.functions.Function2` to `java.util.function.BiFunction`.
- Removes the dependency on Spring Cloud Function and
`net.jodah:typetools` which are replaced by leveraging
Spring Framework `ResolvableType` capabilities.
- Add a Kotlin extension function for
`FunctionCallbackWrapper.Builder.withInputType` allowing to
specify `withInputType<T>()` instead of `withInputType(T::class.java)`.
- Add Kotlin documentation.
Fixes: #1335https://github.com/spring-projects/spring-ai/issues/1335
Add support for maintaining JSON property order in generated schemas using
@JsonPropertyOrder. Users can now control the order of properties in their
JSON schemas by annotating their classes/records with @JsonPropertyOrder.
- Add JacksonOption.RESPECT_JSONPROPERTY_ORDER to BeanOutputConverter
- Add test to verify schema property ordering
- Update documentation with property ordering example
Adds Oracle Cloud Infrastructure (OCI) Generative AI's Cohere chat model support
to expand Spring AI's cloud provider capabilities. This allows developers to use
OCI's managed Cohere models through both dedicated and on-demand serving modes.
The integration provides auto-configuration for simple setup while allowing full
customization of model parameters through OCICohereChatOptions. Teams can now
use OCI's Cohere models alongside other providers in Spring AI applications.
This change complements the existing OCI embedding support, offering a complete
set of GenAI capabilities for Oracle Cloud users.
Signed-off-by: Anders Swanson <anders.swanson@oracle.com>
Add support for proper Kotlin functions handling
by adapting kotlin.jvm.functions.Function1 to
java.util.function.Function and kotlin.jvm.functions.Function2 to
java.util.function.BiFunction.
It also removes the dependency on Spring Cloud Function and
net.jodah:typetools which are replaced by leveraging
Spring Framework ResolvableType capabilities.
Added withInputType Kotlin extension
- Add a Kotlin extension function for
FunctionCallbackWrapper.Builder.withInputType allowing
to specify withInputType<T>() instead of
withInputType(T::class.java).
- Add Kotlin documentation
Introduces support for Amazon Bedrock Converse API through a new BedrockProxyChatModel
implementation. This enables integration with Bedrock's conversation models with features
including:
- Support for sync/async chat completions
- Stream response handling
- Tool/function calling capabilities
- System message support
- Image input support
- Observation and metrics integration
- Configurable model parameters and AWS credentials
Adds core support classes:
- BedrockUsage: Implements Usage interface for token tracking
- ConverseApiUtils: Utility class for handling Bedrock API responses including:
- Tool use event aggregation and processing
- Chat response transformation from stream outputs
- Model options conversion
- Support for metadata aggregation
- URLValidator: Utility for URL validation and normalization with support for:
- Basic and strict URL validation
- URL normalization
- Multimodal input handling
- Enhanced FunctionCallingOptionsBuilder with merge capabilities for both ChatOptions
and FunctionCallingOptions
- Added BEDROCK_CONVERSE to AiProvider enum for metrics tracking
- Extended AWS credentials support with session token capability
- Added configurable session token property to BedrockAwsConnectionProperties
Adds new auto-configuration support:
- BedrockConverseProxyChatAutoConfiguration for automatic setup of the Bedrock Converse chat model
- BedrockConverseProxyChatProperties for configuration including:
- Model selection (defaults to Claude 3 Sonnet)
- Timeout settings (defaults to 5 minutes)
- Temperature and token control
- Top-K and Top-P sampling parameters
- Integration with existing BedrockAwsConnectionConfiguration for AWS credentials
Updates to testing infrastructure:
- Adds comprehensive test suite for Bedrock Converse properties and auto-configuration
- Integration tests for chat completion and streaming scenarios
- Property validation tests for configuration options
- Temporarily disabled other Bedrock tests due to AWS quota limitations
- Added ObjectMapper configuration for proper JSON handling
Added new spring-ai-bedrock-converse-spring-boot-starter module
Updates module configuration in parent POM and BOM to include new bedrock-converse
modules and starters. Adds necessary auto-configuration imports for seamless integration
with Spring Boot applications.
Unrelated changes:
- Disabled several Bedrock model tests (Jurassic2, Llama, Titan) due to AWS quota limitations
- Disabled PaLM2 tests due to API decommissioning by Google
Resolves#809, #802
Add docs and fix configs
- Move timeout configuration from chat properties to connection properties
- Add comprehensive documentation for Bedrock Converse API usage and configuration
- Update tests to reflect configuration changes
Co-authored-by: maxjiang153 <maxjiang153@users.noreply.github.com>
Standardize AWS credential handling in integration tests
- Improve how we manage AWS credentials across our integration test
suite and ensures consistent test configuration. We're replacing individual
environment variable checks with @RequiresAwsCredentials
annotation and standardizing the use of BedrockTestUtils for context creation
in tests
We also align all AWS regions to US_EAST_1 for consistency and add missing
dependency versioning for Oracle Free.
These changes make our AWS tests more easier to maintain.
Key changes:
- Replace @EnabledIfEnvironmentVariable with @RequiresAwsCredentials
- Standardize context creation via BedrockTestUtils
- Set AWS region to US_EAST_1
- Add Oracle Free dependency version in pom.xml
chatclient.adoc
- Replaced javaCopySimpleLoggerAdvisor with SimpleLoggerAdvisor
- Fixed typo in the multiline String for ChatClient example
etl-pipeline.adoc
- Fixed indent in the ParagraphPdfDocumentReader example
imageclient.adoc
- Fixed indent of override annotation in the ImageGeneration example
testing.adoc
- Added a semicolon to the Evaluator interface
Signed-off-by: jitokim <pigberger70@gmail.com>
The PostgresMLEmbeddingModel autoconfiguration previously always executed
"CREATE EXTENSION IF NOT EXISTS pgml" on startup. This could cause issues
for users without superuser privileges or those who manage extensions
through other means.
Added a new configuration property 'createExtension' (default false) to
make this behavior optional. Users can now explicitly enable extension
creation when needed.
Updated documentation to explain the new configuration option and its
implications for deployment.
Add explicit model compatibility information to prevent failed deployments
with unsupported architectures. Point users to standard endpoints as
alternative for unsupported models.
- Update Claude 3.5 Sonnet model version from 20240620 to 20241022 across:
- AnthropicApi model definitions
- Integration tests
- Sample events JSON
- Documentation pages
- Upgrade Ollama container to 0.3.14 in tests
- Add llama3.2:1b model to Ollama tests
- Convert Ollama functionCallTest to parameterized test
- Implement core vector store module for CosmosDB integration
- Add Spring Boot auto-configuration capabilities
- Integrate batch processing strategy for optimized operations
- Include comprehensive tests for core and auto-config modules
- Add reference docs for the CosmosDB vector store support
